A simple approach for protein structure discrimination based on the network pattern of conserved hydrophobic residues

The construction of an amino acid network for understanding protein structure and function

Amino acid networks (AANs) are undirected networks consisting of amino acid residues and their interactions in three-dimensional protein structures. The analysis of AANs provides novel insight into protein science, and several common amino acid network properties have revealed diverse classes of proteins. In this review, we first summarize methods for the construction and characterization of AANs. We then compare software tools for the construction and analysis of AANs. Finally, we review the application of AANs for understanding protein structure and function, including the identification of functional residues, the prediction of protein folding, analyzing protein stability and protein-protein interactions, and for understanding communication within and between proteins.

BeEP Server: Using evolutionary information for quality assessment of protein structure models

The BeEP Server (http://www.embnet.qb.fcen.uba.ar/embnet/beep.php) is an online resource aimed to help in the endgame of protein structure prediction. It is able to rank submitted structural models of a protein through an explicit use of evolutionary information, a criterion differing from structural or energetic considerations commonly used in other assessment programs. The idea behind BeEP (Best Evolutionary Pattern) is to benefit from the substitution pattern derived from structural constraints present in a set of homologous proteins adopting a given protein conformation. The BeEP method uses a model of protein evolution that takes into account the structure of a protein to build site-specific substitution matrices. The suitability of these substitution matrices is assessed through maximum likelihood calculations from which position-specific and global scores can be derived. These scores estimate how well the structural constraints derived from each structural model are represented in a sequence alignment of homologous proteins. Our assessment on a subset of proteins from the Critical Assessment of techniques for protein Structure Prediction (CASP) experiment has shown that BeEP is capable of discriminating the models and selecting one or more native-like structures. Moreover, BeEP is not explicitly parameterized to find structural similarities between models and given targets, potentially helping to explore the conformational ensemble of the native state.

Small world network strategies for studying protein structures and binding

Small world network concepts provide many new opportunities to investigate the complex three dimensional structures of protein molecules. This mini-review explores the published literature on using small-world network approaches to study protein structure, with emphasis on the different combinations of descriptors that have been tested, on studies involving ligand binding in protein-ligand complexes, and on protein-protein complexes. The benefits and success of small world network approaches, which change the focus from specific interactions to the local environment, even to non-local phenomenon, are described. The purpose is to show the different ways that small world network concepts have been used for building new computational models for studying protein structure and function, and for extending and improving existing modelling approaches.

A modified amino acid network model contains similar and dissimilar weight

For a more detailed description of the interaction between residues, this paper proposes an amino acid network model, which contains two types of weight—similar weight and dissimilar weight. The weight of the link is based on a self-consistent statistical contact potential between different types of amino acids. In this model, we can get a more reasonable representation of the distance between residues. Furthermore, with the network parameter, average shortest path length, we can get a more accurate reflection of the molecular size. This amino acid network is a “small-world” network, and the network parameter is sensitive to the conformation change of protein. For some disease-related proteins, the highly central residues of the amino acid network are highly correlated with the hot spots. In the compound with the related drug, these residues either interacted directly with the drug or with the residue which is in contact with the drug.

HYDROGEN BOND NETWORK OF 1-ALKYL-3-METHYLIMIDAZOLIUM IONIC LIQUIDS: A NETWORK THEORY ANALYSIS

Hydrogen bond is a key factor in the determination of structures and properties of room-temperature ionic liquids. Connections of these hydrogen bonds form a network. In this work, we analyzed the hydrogen bond network of 1-alkyl-3-methylimidazolium ionic liquids using network theory. A two-dimensional view of the hydrogen bond network has been generated, the connection pattern shown that the average length of line shape connection is 2.44 to 2.77 for six 1-alkyl-3-methylimidazolium ionic liquids, and the connection patterns are different for short and long alkyl side chain length. The degree of each ion was calculated and analyzed. The nodes with zero degree were adopted to detect the boundary of the clusters in the ionic liquids, which have no hydrogen bond connected with neighbor ions. This work indicates that the network analysis method is useful for understanding and predicting the structure and function of RTILs.

Developing a high-quality scoring function for membrane protein structures based on specific inter-residue interactions

Membrane proteins are of particular biological and pharmaceutical importance, and computational modeling and structure prediction approaches play an important role in studies of membrane proteins. Developing an accurate model quality assessment program is of significance to the structure prediction of membrane proteins. Few such programs are proposed that can be applied to a broad range of membrane protein classes and perform with high accuracy. We developed a new model scoring function Interaction-based Quality assessment (IQ), based on the analysis of four types of inter-residue interactions within the transmembrane domains of helical membrane proteins. This function was tested using three high-quality model sets: all 206 models of GPCR Dock 2008, all 284 models of GPCR Dock 2010, and all 92 helical membrane protein models of the HOMEP set. For all three sets, the scoring function can select the native structures among all of the models with the success rates of 93, 85, and 100% respectively. For comparison, these three model sets were also adopted for a recently published model assessment program for membrane protein structures, ProQM, which gave the success rates of 85, 79, and 92% separately. These results suggested that IQ outperforms ProQM when only the transmembrane regions of the models are considered. This scoring function should be useful for the computational modeling of membrane proteins.

Assortative mixing in close-packed spatial networks

Background

In recent years, there is aroused interest in expressing complex systems as networks of interacting nodes. Using descriptors from graph theory, it has been possible to classify many diverse systems derived from social and physical sciences alike. In particular, folded proteins as examples of self-assembled complex molecules have also been investigated intensely using these tools. However, we need to develop additional measures to classify different systems, in order to dissect the underlying hierarchy.

Methodology and Principal Findings

In this study, a general analytical relation for the dependence of nearest neighbor degree correlations on degree is derived. Dependence of local clustering on degree is shown to be the sole determining factor of assortative versus disassortative mixing in networks. The characteristics of networks constructed from spatial atomic/molecular systems exemplified by self-organized residue networks built from folded protein structures and block copolymers, atomic clusters and well-compressed polymeric melts are studied. Distributions of statistical properties of the networks are presented. For these densely-packed systems, assortative mixing in the network construction is found to apply, and conditions are derived for a simple linear dependence.

Conclusions

Our analyses (i) reveal patterns that are common to close-packed clusters of atoms/molecules, (ii) identify the type of surface effects prominent in different close-packed systems, and (iii) associate fingerprints that may be used to classify networks with varying types of correlations.

A topological description of hubs in amino Acid interaction networks

We represent proteins by amino acid interaction networks. This is a graph whose vertices are the proteins amino acids and whose edges are the interactions between them. Once we have compared this type of graphs to the general model of scale-free networks, we analyze the existence of nodes which highly interact, the hubs. We describe these nodes taking into account their position in the primary structure to study their apparition frequency in the folded proteins. Finally, we observe that their interaction level is a consequence of the general rules which govern the folding process.

Quality assessment of protein model-structures using evolutionary conservation

Motivation: Programs that evaluate the quality of a protein structural model are important both for validating the structure determination procedure and for guiding the model-building process. Such programs are based on properties of native structures that are generally not expected for faulty models. One such property, which is rarely used for automatic structure quality assessment, is the tendency for conserved residues to be located at the structural core and for variable residues to be located at the surface.

Results: We present ConQuass, a novel quality assessment program based on the consistency between the model structure and the protein's conservation pattern. We show that it can identify problematic structural models, and that the scores it assigns to the server models in CASP8 correlate with the similarity of the models to the native structure. We also show that when the conservation information is reliable, the method's performance is comparable and complementary to that of the other single-structure quality assessment methods that participated in CASP8 and that do not use additional structural information from homologs.

Availability: A perl implementation of the method, as well as the various perl and R scripts used for the analysis are available at http://bental.tau.ac.il/ConQuass/.

Contact:nirb@tauex.tau.ac.il

Supplementary information:Supplementary data are available at Bioinformatics online.

Protein structure classification based on conserved hydrophobic residues

Protein folding is frequently guided by local residue interactions that form clusters in the protein core. The interactions between residue clusters serve as potential nucleation sites in the folding process. Evidence postulates that the residue interactions are governed by the hydrophobic propensities that the residues possess. An array of hydrophobicity scales has been developed to determine the hydrophobic propensities of residues under different environmental conditions. In this work, we propose a graph-theory-based data mining framework to extract and isolate protein structural features that sustain invariance in evolutionary-related proteins, through the integrated analysis of five well-known hydrophobicity scales over the 3D structure of proteins. We hypothesize that proteins of the same homology contain conserved hydrophobic residues and exhibit analogous residue interaction patterns in the folded state. The results obtained demonstrate that discriminatory residue interaction patterns shared among proteins of the same family can be employed for both the structural and the functional annotation of proteins. We obtained on the average 90 percent accuracy in protein classification with a significantly small feature vector compared to previous results in the area. This work presents an elaborate study, as well as validation evidence, to illustrate the efficacy of the method and the correctness of results reported.

Comparing four different approaches for the determination of inter-residue interactions provides insight for the structure prediction of helical membrane proteins

Studying inter-residue interactions provides insight into the folding and stability of both soluble and membrane proteins and is essential for developing computational tools for protein structure prediction. As the first step, various approaches for elucidating such interactions within protein structures have been proposed and proven useful. Since different approaches may grasp different aspects of protein structural folds, it is of interest to systematically compare them. In this work, we applied four approaches for determining inter-residue interactions to the analysis of three distinct structure datasets of helical membrane proteins and compared their correlation to the three individual quality measures of structures in these datasets. These datasets included one of 35 structures of rhodopsin receptors and bacterial rhodopsins determined at various resolutions, one derived from the HOMEP benchmark dataset previously reported, and one comprising of 139 homology models. It was found that the correlation between the average number of inter-residue interactions obtained by applying the four approaches and the available structure quality measures varied quite significantly among them. The best correlation was achieved by the approach focusing exclusively on favorable inter-residue interactions. These results provide interesting insight for the development of objective quality measure for the structure prediction of helical membrane proteins.

Analysis of the residue-residue coevolution network and the functionally important residues in proteins

It is a common belief that some residues of a protein are more important than others. In some cases, point mutations of some residues make butterfly effect on the protein structure and function, but in other cases they do not. In addition, the residues important for the protein function tend to be not only conserved but also coevolved with other interacting residues in a protein. Motivated by these observations, the authors propose that there is a network composed of the residues, the residue-residue coevolution network (RRCN), where nodes are residues and links are set when the coevolutionary interaction strengths between residues are sufficiently large. The authors build the RRCN for the 44 diverse protein families. The interaction strengths are calculated by using McBASC algorithm. After constructing the RRCN, the authors identify residues that have high degree of connectivity (hub nodes), and residues that play a central role in network flow of information (C(I) nodes). The authors show that these residues are likely to be functionally important residues. Moreover, the C(I) nodes appear to be more relevant to the function than the hub nodes. Unlike other similar methods, the method described in this study is solely based on sequences. Therefore, the method can be applied to the function annotation of a wider range of proteins.